Linking text characteristics of ideas to their popularity in online user innovation communities

There is a growing trend of organizations deploying online user innovation communities (UIC) to collect innovative ideas from customers or users. When users submit a large number of ideas, screening and reviewing those ideas becomes a cumbersome task. Prior research indicates that, in a UIC, the popularity of an idea is one of the critical factors for getting recognition amidst a large number of ideas. There exists a lack of clarity on how an idea becomes popular in a UIC. Therefore, drawing on the theoretical underpinnings of cognitive overload theory, we develop a conceptual model to help firms understand how the text characteristics of an idea can be a determining force in making it popular. In particular, we study the effect of an idea's length, breadth, and textual dissimilarity to previously submitted ideas, on the likelihood of receiving comments and votes, which constitutes idea popularity. Our model is validated through logistic regression, using secondary data on 5283 users' ideas collected from the online UIC of the Starbucksmyidea platform. We find a significant impact of these characteristics on idea popularity. Implications for theory and practice are discussed for the effective functioning of UIC platforms.